Research within the agricultural community has shown that management of crop production may be optimized by taking into account spatial variations that often exist within a given farming field. For example, by varying the farming inputs applied to a field according to local conditions within the field, a farmer can optimize crop yield as a function of the inputs being applied while preventing or minimizing environmental damage. This management technique has become known as precision, site-specific, prescription or spatially-variable farming.
The management of a field using precision farming techniques requires the gathering and processing of data relating to site-specific characteristics of the field. Generally, site-specific input data is analyzed in real-time or off-line to generate a prescription map including desired application rates of a farming input. A control system reads data from the prescription map and generates a control signal which is applied to a variable-rate controller adapted to apply a farming input to the field at a rate that varies as a function of the location. Variable-rate controllers may be mounted on agricultural vehicles with attached variable-rate applicators, and may be used to control application rates for applying seed, fertilizer, insecticide, herbicide or other inputs. The effect of the inputs may be analyzed by gathering site-specific yield and moisture content data and correlating this data with the farming inputs, thereby allowing a user to optimize the amounts and combinations of farming inputs applied to the field.
The spatially-variable characteristic data may be obtained by manual measuring, remote sensing, or sensing during field operations. Manual measurements typically involve taking a soil probe and analyzing the soil in a laboratory to determine nutrient data or soil condition data such as soil type or soil classification. Taking manual measurements, however, is labor intensive and, due to high sampling costs, provides only a limited number of data samples. Remote sensing may include taking aerial photographs or generating spectral images or maps from airborne or spaceborne multispectral sensors. Spectral data from remote sensing, however, is often difficult to correlate with a precise position in a field or with a specific quantifiable characteristic of the field. Both manual measurements and remote sensing require a user to conduct an airborne or ground-based survey of the field apart from normal field operations.
Spatially-variable characteristic data may also be acquired during normal field operations using appropriate sensors supported by a combine, tractor or other vehicle. A variety of characteristics may be sensed including soil properties (e.g., organic matter, fertility, nutrients, moisture content, compaction and topography or altitude), crop properties (e.g., height, moisture content or yield), and farming inputs applied to the field (e.g., fertilizers, herbicides, insecticides, seeds, cultural practices or tillage techniques used). Other spatially-variable characteristics may be manually sensed as a field is traversed (e.g., insect or weed infestation or landmarks). As these examples show, characteristics which correlate to a site-specific location include data related to local conditions of the field, farming inputs applied to the field, and crops harvested from the field.
Obtaining characteristic data during normal field operations is advantageous over manual measuring and remote sensing because less labor is required, more data samples may be taken, samples may be easier to correlate with specific positions and separate field surveys are not required. However, gathered data will be accurate and complete only if the agricultural vehicle is driven over each location in the field. Providing an electronic display in the vehicle cab which shows a map of the area being traversed would provide feedback to a farmer while working the field. Such an electronic display would help the farmer to determine the portions of the field which have been worked and to plan an efficient path over the remaining area. Also, providing a display with visible indicia of a characteristic being measured would promote monitoring of the characteristic as the field is worked.
Providing an easy-to-read electronic display showing a map of the area being traversed correlated with visual indicia of a characteristic being measured is difficult because of the scaling requirements of such a display. Determining the scale is difficult because the boundaries of the area being displayed change as the area is defined by location data received during travel of the vehicle. The problem may be more difficult where the boundaries of the field are unknown since no manual or remote survey of the field has been performed, or the data from such a survey is not in a format usable by the display system. Such a display should include visible indicia of the characteristic being sampled which surrounds the vehicle in every direction to show how the characteristic varies throughout the field.
Present agricultural mapping systems do not address these problems. In one system, the operator of an agricultural vehicle is provided with a non-graphical display including information such as instantaneous yield, moisture content and GPS status. However, the display does not show a graphical map of the field, and does not allow an operator to monitor the characteristic except at the location where he is currently located.